An AI Framework for Brain Tumor Classification, Localization, and Clinical Assistance

DOI

10.2991/978-94-6239-799-6_6How to use a DOI?

Keywords

Brain Tumor Detection; Magnetic Resonance Imaging (MRI); Deep Learning; Convolutional Neural Networks; U-Net Segmentation; Transfer Learning; Explainable AI; Clinical Decision Support Systems

Abstract

Among various neurological diseases, brain tumors pose a significant challenge due to their complexity and the need for prompt and reliable diagnosis to support effective therapeutic interventions and improve patient prognosis. Magnetic Resonance Imaging (MRI) has become the preferred technique for examining intracranial abnormalities because of its superior soft-tissue contrast and non-invasive nature. Nevertheless, analyzing MRI scans manually requires considerable clinical expertise, is labor-intensive, and may result in inconsistencies among medical professionals. To overcome these limitations, the present work introduces a unified artificial intelligence framework that integrates a VGG16 transfer learning model for tumor classification, a U-Net architecture for lesion segmentation, Grad-CAM-based visual interpretability, and an intelligent conversational assistant into a single web-enabled diagnostic environment. The developed system simultaneously carries out brain tumor identification, accurate lesion delineation, prediction interpretability, and AI-assisted user interaction through a unified web-based platform. The combination of these integrated components facilitates efficient MRI analysis, increases the transparency of automated diagnostic outcomes, and delivers dependable clinical decision-support for medical practitioners.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Priyanka Kumari
AU  - Sneha Misra
AU  - Parthib Das
AU  - Rupak Das
AU  - Arnab Adhikary
AU  - Astarag Diasi
PY  - 2026
DA  - 2026/09/30
TI  - An AI Framework for Brain Tumor Classification, Localization, and Clinical Assistance
BT  - Proceedings of the International Conference on Emerging Trends and Technologies in Applications of Computer Science (ICETTACS 2026)
PB  - Atlantis Press
SP  - 63
EP  - 83
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-799-6_6
DO  - 10.2991/978-94-6239-799-6_6
ID  - Kumari2026
ER  -